Failure diagnosis system, image forming apparatus, computer readable medium and data signal

ABSTRACT

A failure diagnosis system includes a first database, a second database, an observation-information acquiring unit, a determination-probability calculating unit and an occurrence-probability calculating unit. A first cause-and-effect network stored in the first database stochastically represents a relationship between failure-type candidates and first observation information, which have cause-and-effect relationships with the failure-type candidates. Each second cause-and-effect network stored in the second database stochastically represents relationships between second observation information and failure-cause candidates. The observation-information acquiring unit acquires the first and second observation information from a diagnosed system. The determination-probability calculating unit calculates a determination probability of each failure-type candidate based on the first observation information and the first cause-and-effect network. The occurrence-probability calculating unit calculates occurrence probabilities of the failure-cause candidates for each failure-type candidate based on the second observation information and the second cause-and-effect networks.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based on and claims priority under 35 U.S.C. §119from Japanese Patent Application No. 2006-160373 filed Jun. 9, 2006.

BACKGROUND

1. Technical Field

This invention relates to a failure diagnosis system, an image formingapparatus and a computer readable medium storing a failure diagnosisprogram and a data signal embedded with the failure diagnosis program.

2. Related Art

Hitherto, in office machines such as a copier and a printer, aprofessional serviceperson has been dispatched for executing periodicmaintenance to maintain good quality. However, the manner of a failurehas also become complicated with colorization and advanced function ofoffice machines in recent years. In some cases, even the professionalserviceperson cannot determine the cause of the failure and it isnecessary to lessen the down time of client's machine as much aspossible. Therefore, the case frequently occurs where plural partsseeming to be involved in the failure are replaced collectively. Normalparts are replaced together, resulting in an increase in the servicecost.

SUMMARY

According to an aspect of the invention, a failure diagnosis systemincludes a first cause-and-effect network, plural secondcause-and-effect networks, an observation-information acquiring unit, adetermination-probability calculating unit, an occurrence-probabilitycalculating unit and a failure-cause notifying unit. The first databasestores a first cause-and-effect network. The first cause-and-effectnetwork stochastically represents a relationship between a plurality offailure-type candidates and first observation information, which havecause-and-effect relationships with the respective failure-typecandidates. The second database stores a plurality of secondcause-and-effect networks for the respective failure-type candidates.Each of the second cause-and-effect networks stochastically representsrelationships between second observation information and thefailure-cause candidates. The observation-information acquiring unitacquires the first observation information and the second observationinformation from a diagnosed system. The determination-probabilitycalculating unit calculates a determination probability of eachfailure-type candidate based on the first observation informationacquired by the observation-information acquiring unit and the firstcause-and-effect network. The occurrence-probability calculating unitcalculates occurrence probabilities of the failure-cause candidates foreach failure-type candidate based on the second observation informationacquired by the observation-information acquiring unit and the secondcause-and-effect networks. The failure-cause notifying unit notifies atleast one of the failure-cause candidates as a failure cause, based onthe determination probabilities of the respective failure-typecandidates calculated by the determination-probability calculating unitand the occurrence probabilities of the respective failure-causecandidates calculated by the occurrence-probability calculating unit.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the invention will be described below in detailwith reference to the accompanying drawings wherein:

FIG. 1 is a block diagram to schematically show an image formingapparatus according to an exemplary embodiment of the invention;

FIG. 2 is a block diagram to schematically show one configurationexample of a failure diagnosis module;

FIG. 3 is a block diagram to schematically show one configurationexample of a failure-probability inferring module;

FIG. 4 is a drawing to show a configuration example of a Bayesiannetwork for conducting a failure diagnosis of an image defect system;

FIG. 5 is a drawing to show a configuration example of the Bayesiannetwork in the configuration example of the failure diagnosis of theimage defect when a black line occurs;

FIG. 6 is a drawing to show a configuration example of the Bayesiannetwork in the configuration example of the failure diagnosis of theimage defect when a point occurs;

FIG. 7 is a drawing to show a configuration example of the Bayesiannetwork in the configuration example of the failure diagnosis of theimage defect when scumming occurs;

FIG. 8 is a flowchart to show an example of a process procedure of afailure diagnosis system;

FIG. 9 is a flowchart to show an example of a process procedure forcalculating occurrence probabilities of failure causes of image defects;

FIG. 10 is a drawing to show a configuration example of the Bayesiannetwork for calculating determination probabilities of defect types; and

FIG. 11 is a table to describe the example of the procedure forcalculating the occurrence probabilities of the failure causes.

DETAILED DESCRIPTION

Referring now to the accompanying drawings, exemplary embodiments of theinvention will be described.

FIG. 1 is a schematic module configuration diagram according to oneexemplary embodiment of the invention.

A “module” generally refers to a logically detachable part of softwareor hardware. The “module” in this exemplary embodiment means not only amodule in a program, but also a module in the hardware configuration.Therefore, the exemplary embodiment servers as description on a program,a system and a method. Modules are almost in a one-to-one correspondencewith functions. However, in implementation, one module may be oneprogram; plural modules may be formed of one program; or plural programsmay make up one module. Plural modules may be executed by one computer;or one module may be executed in plural computers in a distributed orparallel environment. In the following description, a “connection”contains not only physical connection, but also logical connection.

A “system” is not only a system made up of plural computers, pluralhardware and/or plural devices, which are connected by a network, butalso a system implemented as one computer.

This exemplary embodiment will be described mainly by illustrating animage forming apparatus to which a failure diagnosis system is applied.A specific image forming apparatus includes at least a printer, acopier, a facsimile or a multiple function machine (also called amultifunctional copier and having functions of a scanner, a printer, acopier and a facsimile).

This exemplary embodiment shows the determination accuracy of a defecttype stochastically, calculate the failure probability on which thedetermination accuracies in plural diagnosis models are reflected,combining the probabilities of the failure causes common to thediagnosis models, and outputting the results in the probability order(from high to low) as a whole. If sufficient observation data requiredfor determining the failure type cannot be acquired and an erroneousdetermination occurs, it is possible to totally determine the failurecause by reflecting the diagnosis result from plural diagnosis modelswithout a diagnosis model being uniquely determined. Consequently, it ispossible to conduct more accurate diagnosis.

An image forming apparatus 1 includes: an image reading module 101 forreading an original image; a print engine module 102 for forming andoutputting the read image or an image whose print is commanded; a sensormodule 103 containing one or more sensors for obtaining internal statusinformation of the image forming apparatus 1 such as a paper passagetime, a drive current and apparatus internal temperature/humidity; adiagnosis information input module 104 for inputting informationrequired for failure diagnosis; a failure diagnosis module 105 forconducting a failure diagnosis of the apparatus based on the acquiredinformation; a communication module 106 connected to an external center110 via a communication line; and a bus 107 for connecting the modules.

To conduct the failure diagnosis of the image forming apparatus 1, theprint engine module 102 outputs a sheet of output paper 100 and inputsthe sheet of output paper 100 to the image reading module 101.

The failure diagnosis result may be displayed for the operator of theimage forming apparatus 1 or may be reported to the center 110 throughthe communication module 106.

FIG. 2 is a block diagram to schematically show one configurationexample of the failure diagnosis module 105.

A parts-status-information acquiring module 201 acquires, as observationdata information, parts information indicating an operation status ofeach part based on the internal status information of the image formingapparatus 1 acquired by the sensor module 103.

A history-information acquiring module 202 acquires, as historyinformation, a monitoring result of a usage status of the image formingapparatus 1. For example, the history information indicates that when,how many sheets of paper and which size of paper the image formingapparatus 1 output.

An environmental-information acquiring module 203 directly acquiresenvironmental information of the image forming apparatus 1 or acquiresenvironmental information of the image forming apparatus 1 acquired bythe sensor module 103.

An image-defect-type-determination-information extracting module 204compares the image read by the image reading module 101 (inspectedimage) with a reference image for inspection, analyzes a defectivecondition of the output image, and extracts information required fordetermining a defect type (e.g., defect in area, defect in shape ordefect in density).

A feature-amount extracting module 205 extracts various feature amounts(e.g., line width information, periodicity information and occurrenceportion information) from the analysis result generated by theimage-defect-type-determination-information extracting module 204.

An additional-operation-information acquiring module 206 acquiresfailure information under a different condition input by a user'soperation.

A failure-probability inferring module 207 infers a failure cause basedon the information provided by the modules, namely, theparts-status-information acquiring module 201, the history-informationacquiring module 202, the environmental-information acquiring module203, the image-defect-type-determination-information extracting module204, the feature-amount extracting module 205 and theadditional-operation-information acquiring module 206. With regard tothe parts-status-information acquiring module 201, thehistory-information acquiring module 202, the environmental-informationacquiring module 203, the image-defect-type-determination-informationextracting module 204, the feature-amount extracting module 205 and theadditional-operation-information acquiring module 206, at least one ofthe information provided by the modules 201 to 206 is used, and it isnot necessary to use all the information.

A diagnosis-result notifying module 208 notifies the user of thediagnosis result, namely, the failure cause.

Further, the failure-probability inferring module 207 has afailure-candidate extracting module 209 and an inference engine 210.

The inference engine 210 calculates, for example, a probability thateach cause candidate causing a failure is the main cause of the failure(failure cause probability), based on the acquired information.

The failure-candidate extracting module 209 narrows down the failurecause candidates based on the failure cause probability calculated bythe inference engine 210.

The Bayesian network is used in the inference engine 210 to calculatethe failure cause probability. The “Bayesian network” represents aproblem area, which is complicated in a cause-and-effect relationship,by means of a network having a graph structure obtained by connectingthe cause-and-effect relationships among variables. The “Bayesiannetwork” represents dependence relationships among the variables with adirected graph. Alternatively, the failure-probability inferring module207 may infer the failure cause using a case-based expert system oradopt a method using a neural network.

FIG. 3 is a block diagram to schematically show one configurationexample of the failure-probability inferring module 207. Thefailure-probability inferring module 207 includes adetermination-probability calculating module 302, a first Bayesiannetwork 303, an occurrence-probability calculating module 304, a secondBayesian network 305, a multiplication module 306, and a sum-totalcalculating module 307.

An observation-information acquiring module 301 corresponds to any or acombination of the parts-status-information acquiring module 201, thehistory-information acquiring module 202, the environmental-informationacquiring module 203, the image-defect-type-determination-informationextracting module 204 and the feature-amount extracting module 205. Thismeans that the failure-probability acquiring module 207 acquires pluralpieces of observation information from the image forming apparatus 1,which is a system to be diagnosed.

The first Bayesian network 303 is stored in a storage device such as ahard disk and stochastically represents a relationship among pluralfailure types and the observation information, which have thecause-and-effect relations with the respective failure types.

The determination-probability calculating module 302 calculates adetermination probability of each failure-type candidate based on (i)the observation information acquired by the observation-informationacquiring module 301 and (ii) the first Bayesian network 303.

The second Bayesian network 305 is stored in a storage device such as ahard disk. The second Bayesian network 305 is plural second Bayesiannetworks for stochastically representing, for each failure type,relationships between the observation information of the failure typeand the failure causes.

The occurrence-probability calculating module 304 calculates anoccurrence probability of each failure cause candidate for each failuretype based on (i) the observation information acquired by theobservation-information acquiring module 301 and (ii) the secondBayesian network 305.

The multiplication module 306 multiplies (i) the determinationprobability calculated by the determination-probability calculatingmodule 302 by (ii) the occurrence probability of each failure causecandidate calculated by the occurrence-probability calculating module304.

The sum-total calculating module 307 calculates a sum total of themultiplication results, provided by the multiplication module 306, ofeach failure cause candidate common to the second Bayesian networks 305.

The diagnosis-result notifying module 208 notifies the user of thefailure causes in order of the sum totals calculated by the sum-totalcalculating module 307 (from high to low).

FIG. 4 shows ideally a configuration example of the Bayesian network forconducting the failure diagnosis of the image defect system. As shown inFIG. 4, the Bayesian network includes: a failure cause node ND0representing a cause of an image defect; a component status node ND1representing status information of the components making up the imageforming apparatus 1; a history information node ND2 representing thehistory information of the image forming apparatus 1; an environmentinformation node ND3 representing information (environment information)of an ambient environment in which the image forming apparatus 1 isinstalled; an observation status node ND4 representing the statusinformation of the image defect; a user operation node ND5 representingadditional result information obtained by the user's operation; and adefect type node ND6.

The failure cause node ND0 is a node representing the cause of an imagedefect. Whether or not a failure occurs is determined by calculating theprobability of the failure cause node ND0. Each node stores aprobability table listing probability data, which represents thestrength of the cause-and-effect relationships. The initial values ofthe probability data may be determined using data at the time when pastfailures occurred and MTBF (Mean Time Between Failures) of parts.

The component status node ND1 is a node representing the statuses of therespective components and is information acquired from the sensor module103, which observes the statuses of the respective components. Suchinformation may include component temperature, applied voltage, batchdensity and color material (for example, toner) remaining amount.

The history information node ND2 represents the usage status of theimage forming apparatus 1. For example, the usage status may be ahistory of the number of print sheets in the past for each component.The number of print sheets directly affects the status of each componentsuch as abrasion and degradation of the component.

The environment information node ND3 is the ambient environmentconditions affecting the statuses of the respective components. Thetemperature and the humidity may correspond to the ambient environmentconditions. The temperature and the humidity affect the image formationcondition and the operation condition of each component.

The observation status node ND4 represents the observation status of adefect occurring in the output image and is information observed andinput by the user. For example, the information input by the user mayinclude information of the result shape, size, density, contours,direction (orientation), position, periodicity and occurrence area.

The user operation node ND5 is information for causing the image formingapparatus 1 to perform a similar process with the operation conditionbeing changed. For example, such information may include information ofthe operation condition after change.

The defect type node ND6 represents types of image defects and mayinclude information of line, point, white patch and density unevenness.First, a type of an image defect, which occurred, is determined, thestatus of the node is determined, and then information of the othernodes (ND1 to ND5) is input to the ND6 appropriately, a diagnosis isconducted, and the failure cause is estimated.

The nodes are connected so as to become the relationship of“cause”->“result.” That is, the nodes are connected so that the failurecause node ND0->the observation status node ND4, the failure cause nodeND0->the defect type node ND6, the failure cause node ND0->the useroperation node ND5, the history information node ND2->the failure causenode ND0, the component status node ND1->the failure cause node ND0, theenvironment information node ND3->the failure cause node ND0.

For example, the relationship between “the failure cause node ND0” and“the observation status node ND4” becomes such a relationship that“observation status (thin density, stripe and belt)” appears based onthe “cause.” On the other hand, the relationship between “the historyinformation node ND2” and “the failure cause node ND0” becomes such arelationship that “cause (parts degradation)” occurs due to “statusbased on history information (the number of copies is large and/or thenumber of operation years is long).”

FIG. 5 shows a specific example of a failure diagnosis model in thefailure diagnosis system, and represents the Bayesian network when ablack line appears due to an image defect in the configuration exampleof the failure diagnosis. The nodes are connected so as to become therelationship of “cause”->“result” as with FIG. 4. For example, therelationship between “flaw of the drum C3” and “line width information”becomes such a relationship that “flaw of the drum C3” is a cause and“line width information” indicating occurrence of a thin line appears.On the other hand, the relationship between “number-of-feed historyinformation” and “fuser” becomes such a relation that a status based onthe “number of feeds” (the number of feeds is equal to or greater thanwhat number) is a cause and the possibility of occurrence of a blackline caused by degradation of the “fuser” increases.

Likewise, FIG. 6 shows the Bayesian network at the time when a point,which is an image defect, occurs. For example, FIG. 6 shows such arelationship that “dirt of a platen” is a cause and a point caused by“image input (scanner) system” occurs. FIG. 7 shows the Bayesian networkat the time when scumming, which is an image defect, occurs. Forexample, FIG. 7 shows such a relationship that “dirt of a heat roll” isa cause and scumming having a period corresponding to the outerperiphery of the heat roll occurs.

The initial value of the probability data of each node is determinedbased on the past data, for example. Thereafter, the probability of eachnode may be updated at regular time intervals based on statistic data ofmarket trouble such as part replacement frequency and defectivecondition occurrence frequency. A status of each node representing theimage defect features such as “line width information,” “periodicityinformation,” and “occurrence point information” shown in FIGS. 5, 6,and 7 is determined based on the feature amounts obtained by theparts-status-information acquiring module 201, the history-informationacquiring module 202 and the environmental-information acquiring module203 shown in FIG. 2.

Next, the operation will be described. An outline of a process procedureof the failure diagnosis in association with an image defect will bedescribed with a flowchart of FIG. 8.

First, at step S801, the user changes the image forming apparatus 1 to afailure diagnosis mode through an operation screen, and the print enginemodule 102 of the image forming apparatus 1 outputs a test pattern forfailure diagnosis (a sheet of output paper 100). The test pattern outputhere may be stored in the print engine module 102 shown in FIG. 1 inadvance. If a cause of the failure is a part of the print engine module102, the defect is reproduced on the test pattern, but if the cause is apart of the image reading module 101 such as a defect only occurring incopying, the defect is not reproduced on the test pattern. However, ifthe cause is a part of the image reading module 101, when the testpattern is placed on the image reading module 101 and an output image isread, the defect appears on the read image. Therefore, the systeminquires of the user as to whether or not the defect occurs only incopying, through the operation screen before the output image is read.The system allows the user to selectively input information regardingthis inquiry. The additional-operation-information acquiring module 206acquires and inputs the selected information to the failure-probabilityinferring module 207.

At step S802, when the print engine module 102 of the image formingapparatus 1 discharges the test pattern, the user places the testpattern on the image reading module 101, which then reads the outputimage.

Next, at step S803, the image-defect-type-determination-informationextracting module 204 of the failure diagnosis module 105 compares theread image with the reference image, which is stored in the imageforming apparatus 1 in advance, to check as to whether or not an imagedefect exists in the read image.

At step S804, if a defect is not detected at step S803 (N at step S804),there is a possibility that the previous defect might occur accidentallyor may be already resolved by conducting some treatment before the testpattern is output. Therefore, the diagnosis result notifying module 208notifies the user of such a fact through the operation screen, and theprocess is terminated. On the other hand, if a defect is detected (Y atstep S804), the process goes to step S805.

At step S805, the image-defect-type-determination-information extractingmodule 204 extracts the feature amount required to determine a defecttype.

Next, at step S806, the feature-amount extracting module 205 extracts adefect feature amount required for diagnosis conducted by the diagnosismodel of each defect type.

Further, at step S807, the parts-status-information acquiring module201, the history-information acquiring module 202 and theenvironmental-information acquiring module 203 acquire various pieces ofdata required for failure diagnosis, such as (i) the status informationof the parts making up the image forming apparatus 1, (ii) the historyinformation of a counter value indicating the number of print sheets foreach part, and (iii) the environment information such as the temperaturein the apparatus and the humidity in the apparatus.

At step S808, when the failure-probability inferring module 207 receivesdata from the image-defect-type-determination-information extractingmodule 204, the feature-amount extracting module 205, theparts-status-information acquiring module 201, the history-informationacquiring module 202, and the environmental-information acquiring module203, the failure-probability inferring module 207 calculates theoccurrence probability of each failure cause using the inference engine210. Details of the process at step S808 will be described later.

At step S809, the failure-candidate extracting module 209 extractsfailure causes as many as the designated number of candidates in theprobability order of the failure cause (from high to low) based on thecalculated probabilities. The user may be allowed to set the number ofcandidates in advance or to input any desired number before thecandidates are extracted.

At step S810, the diagnosis-result notifying module 208 displays thediagnosis result for the user on a display device such as a controlpanel. If the failure cause candidates has be narrowed down at thisstage (No at step S811), the process is terminated.

However, in such automatic determination process, the failure causecandidates may not always be narrowed down to one at this point in time.If the failure cause candidates cannot be narrowed down to one at thispoint in time (Yes at step S811), a user further selects an additionaloperation item required for the failure diagnosis through the operationscreen, and the operation condition of the image forming apparatus 1 ischanged in accordance with the selected item and the print engine module102 again outputs an image.

At step S812, the user inputs information of the additional trial resultthrough the operation screen. The additional operation at this time maybe scaling up or down of an image or be outputting of a test pattern.The additional operation is intended to check presence or absence ofchange in the defect occurrence status. Therefore, the additional trialresult is at a level at which the user can easily input the additionaltrial result in accordance with a question displayed on the operationscreen. The added information and the information, which has alreadybeen input, are collected and the failure cause probability isre-calculated and the failure candidates are narrowed down from theresult. If the failure candidates can be narrowed down or if thereremains no information to be added although the failure candidates arenot narrowed down (No at step S811), the process is terminated.

Next, the process of calculating the occurrence probability of eachfailure cause described at step S808 will be described in detail withreference to FIGS. 9 to 11. FIG. 9 is a flowchart to show the process ofcalculating the occurrence probability of each failure cause.

At step S901, first the failure-probability inferring module 207calculates the occurrence probability of each defect type (determinationprobability of each failure-type candidate) based on the feature amounts(e.g., defect in area, defect in shape or defect in density) receivedfrom the image-defect-type-determination-information extracting module204. At this time, to calculate the occurrence probability of eachdefect type, the failure-probability inferring module 207 uses a defecttype determination model formed of the Bayesian network, which isconstructed based on the cause-and-effect relations between n defecttypes and m feature amounts as shown in FIG. 10. the failure-probabilityinferring module 207 gives to feature nodes Fi (i=1 to m) of the defecttype determination model, evidence information based on the featureamounts received from the image-defect-type-determination-informationextracting module 204. Then, the failure-probability inferring module207 calculates occurrence probability of each defect type P (Mi) (i=1 ton). Alternatively, the failure-probability inferring module 207 may usea mode formed of a neural network.

Next, at step S902, with regard to each of l types of failure causes intotal, the failure-probability inferring module 207 calculates theoccurrence probabilities of the defect types P(Cj) (j=1 to l)(probabilities that the respective failure cause candidates occur),which are contained in the diagnosis models for the respective defecttypes, based on the data received from the feature-amount extractingmodule 205, the parts-status-information acquiring module 201, thehistory-information acquiring module 202 and theenvironmental-information acquiring module 203. FIG. 11 is a tableshowing an example of failure cause occurrence probability calculationexamples related to the diagnosis models of the defect types shown inFIGS. 5 to 7. As shown in FIG. 11, first the failure-probabilityinferring module 207 calculates the occurrence probabilities of thefailure causes included in each diagnosis model.

Next, at step S903, the multiplication module 306 multiplies theoccurrence probability of each defect type by the failure causeprobability calculated for each diagnosis model of the defect type.

At step S904, the sum-total calculating module 307 calculates a sumtotal ΣP(Mi)·P(Cij) of the multiplication values of the probabilitiescalculated at step S903, for each failure cause common to the diagnosismodels for the respective defect types as shown in FIG. 11. Each blankcell in the table of FIG. 11 represents that no diagnosis model of thecorresponding failure cause exists and that the probability of theportion is 0%. The diagnosis-result notifying module 208 notifies theuser of the failure causes in the descending order of the sum totals ofthe probability values at step S810 based on the calculation results.

The exemplary embodiment described relates to the failure diagnosis inthe image forming apparatus. However, the invention is not limitedthereto. The invention may be applied to any other failure diagnosis,and particularly to an apparatus for determining a type of defectdetected by surface inspection of a specimen in a manufacturing processline of the specimen such as a semiconductor wafer or a liquid crystalglass substrate.

The described program may also be stored in a computer readablerecording medium.

The term “computer readable recording medium storing a program” is usedto mean a recording medium which can be read by a computer, which storesthe program, which is used to install and execute the program and whichis used to distribute the program.

The record media may include “DVD-R, DVD-RW and DVD-RAM” of digitalversatile disk (DVD) and standard laid down in DVD Forum, “DVD+R,DVD+RW, etc.,” of standard laid down in DVD+RW, read-only memory(CD-ROM), CD recordable (CD-R), CD rewritable (CD-RW), etc., of compactdisk (CD), magneto-optical disk, flexible disk (FD), magnetic tape, harddisk, read-only memory (ROM), electrically erasable and programmableread-only memory (EEPROM), flash memory, random access memory (RAM),etc., for example.

The described program or a part thereof can be recorded in any of thedescribed record media for retention, distribution, etc. The describedprogram or a part thereof can also be transmitted by communicationsusing a transmission medium such as a wired network used with a localarea network, a metropolitan area network (MAN), a wide area network(WAN), the Internet, an intranet, an extranet, etc., or a wirelesscommunication network or a combination thereof, etc., for example, andcan also be carried over a carrier wave.

Further, the described program may be a part of another program or maybe recorded in a recording medium together with a different program.

The foregoing description of the exemplary embodiments of the inventionhas been provided for the purposes of illustration and description. Itis not intended to be exhaustive or to limit the invention to theprecise forms disclosed. Obviously, many modifications and variationswill be apparent to practitioners skilled in the art. The embodimentswere chosen and described in order to best explain the principles of theinvention and its practical applications, thereby enabling othersskilled in the art to understand the invention for various embodimentsand with the various modifications as are suited to the particular usecontemplated. It is intended that the scope of the invention be definedby the following claims and their equivalents.

1. A failure diagnosis system comprising: a first database that stores afirst cause-and-effect network, wherein the first cause-and-effectnetwork stochastically represents a relationship between a plurality offailure-type candidates and first observation information, which havecause-and-effect relationships with the respective failure-typecandidates, and a second database that stores a plurality of secondcause-and-effect networks for the respective failure-type candidates,wherein each of the second cause-and-effect networks stochasticallyrepresents relationships between second observation information and thefailure-cause candidates; an observation-information acquiring unit thatacquires the first observation information and the second observationinformation from a diagnosed system; a determination-probabilitycalculating unit that calculates a determination probability of eachfailure-type candidate based on the first observation informationacquired by the observation-information acquiring unit and the firstcause-and-effect network; an occurrence-probability calculating unitthat calculates occurrence probabilities of the failure-cause candidatesfor each failure-type candidate based on the second observationinformation acquired by the observation-information acquiring unit andthe second cause-and-effect networks; and a failure-cause notifying unitthat notifies at least one of the failure-cause candidates as a failurecause, based on the determination probabilities of the respectivefailure-type candidates calculated by the determination-probabilitycalculating unit and the occurrence probabilities of the respectivefailure-cause candidates calculated by the occurrence-probabilitycalculating unit.
 2. The system according to claim 1, furthercomprising: a multiplication unit that multiplies (i) the determinationprobability of each failure-type candidate by (ii) the respectiveoccurrence probabilities of the failure-cause candidates for thefailure-type candidate, wherein: the failure-cause notifying unitnotifies the at least one of the failure-cause candidates based onmultiplication results calculated by the multiplication unit.
 3. Thesystem according to claim 2, further comprising: a sum-total calculatingunit that calculates a sum total of the multiplication results,calculated by the multiplication unit, of each failure-cause candidatecommon to the second cause-and-effect networks, wherein: thefailure-cause notifying unit notifies at least part of the failure-causecandidates in descending order of the total sums calculated by thesum-total calculating unit.
 4. The system according to claim 1, furthercomprising: a multiplication unit that calculates P(Mi)×P(Cij) (i=1, 2,. . . n; and j=1, 2, . . . m), where: n denotes natural number; mdenotes natural number; Mi denotes the respective failure-typecandidates; Cij denotes the respective failure-cause candidates for thecorresponding failure-type candidate Mi; P(Mi) denotes the respectivedetermination probabilities of the failure-type candidates Mi; andP(Cij) denotes the respective occurrence probabilities of thefailure-cause candidates Cij, wherein: the failure-cause notifying unitnotifies the at least one of the failure-cause candidates based onP(Mi)×P(Cij) calculated by the multiplication unit.
 5. The systemaccording to claim 4, further comprising: a sum-total calculating unitthat calculates ${\sum\limits_{f}\; {{P({Mi})} \times {P({Cij})}}},$ wherein: the failure-cause notifying unit notifies at least part of thefailure-cause candidates in descending order of$\sum\limits_{f}\; {{P({Mi})} \times {P({Cij})}}$  calculated by thesum-total calculating unit.
 6. An image forming apparatus comprising: aprint engine unit that forms an image on a sheet of paper; and thesystem according to claim
 1. 7. A computer readable medium storing aprogram causing a computer to execute a process for a failure diagnosis,the process comprising: acquiring first observation information andsecond observation information from a diagnosed system; calculating adetermination probability of each failure-type candidate based on theacquired first observation information and a first cause-and-effectnetwork, wherein the first cause-and-effect network stochasticallyrepresents a relationship between the plurality of failure-typecandidates and the first observation information, which havecause-and-effect relationships with the respective failure-typecandidates; calculating occurrence probabilities of failure-causecandidates for each failure-type candidate based on the acquired secondobservation information and a plurality of second cause-and-effectnetworks for the respective failure-type candidates, wherein each secondcause-and-effect network stochastically represents relationships betweenthe second observation information of the failure-type candidate and thefailure-cause candidates; and notifying at least one of thefailure-cause candidates as a failure cause, based on the calculateddetermination probabilities of the respective failure-type candidatesand the calculated occurrence probabilities of the respectivefailure-cause candidates.
 8. A computer data signal embodied in acarrier wave for enabling a computer to perform a process for a failurediagnosis, the process comprising: acquiring first observationinformation and second observation information from a diagnosed system;calculating a determination probability of each failure-type candidatebased on the acquired first observation information and a firstcause-and-effect network, wherein the first cause-and-effect networkstochastically represents a relationship between the plurality offailure-type candidates and the first observation information, whichhave cause-and-effect relationships with the respective failure-typecandidates; calculating occurrence probabilities of failure-causecandidates for each failure-type candidate based on the acquired secondobservation information and a plurality of second cause-and-effectnetworks for the respective failure-type candidates, wherein each secondcause-and-effect network stochastically represents relationships betweenthe second observation information of the failure-type candidate and thefailure-cause candidates; and notifying at least one of thefailure-cause candidates as a failure cause, based on the calculateddetermination probabilities of the respective failure-type candidatesand the calculated occurrence probabilities of the respectivefailure-cause candidates.